Query-Focused Summarization (QFS) summarizes a document cluster in response to a specific input query. QFS algorithms must combine query relevance assessment, central content identification, and redundancy avoidance. Frustratingly, state of the art algorithms designed for QFS do not significantly improve upon generic summarization methods, which ignore query relevance, when evaluated on traditional QFS datasets. We hypothesize this lack of success stems from the nature of the dataset. We define a task-based method to quantify topic concentration in datasets, i.e., the ratio of sentences within the dataset that are relevant to the query, and observe that the DUC 2005, 2006 and 2007 datasets suffer from very high topic concentration. We introduce TD-QFS, a new QFS dataset with controlled levels of topic concentration. We compare competitive baseline algorithms on TD-QFS and report strong improvement in ROUGE performance for algorithms that properly model query relevance as opposed to generic summarizers. We further present three new and simple QFS algorithms, RelSum, ThresholdSum, and TFIDF-KLSum that outperform state of the art QFS algorithms on the TD-QFS dataset by a large margin.

Update summarization is a form of multidocument summarization where a document set must be summarized in the context of other documents assumed to be known.Efficient update summarization must focus on identifying new information and avoiding repetition of known information.In Query-focused summarization, the task is to produce a summary as an answer to a given query.We introduce a new task, Query-Chain Summarization, which combines aspects of the two previous tasks: starting from a given document set, increasingly specific queries are considered, and a new summary is produced at each step.This process models exploratory search: a user explores a new topic by submitting a sequence of queries, inspecting a summary of the result set and phrasing a new query at each step.We present a novel dataset comprising 22 querychains sessions of length up to 3 with 3 matching human summaries each in the consumerhealth domain.Our analysis demonstrates that summaries produced in the context of such exploratory process are different from informative summaries.We present an algorithm for Query-Chain Summarization based on a new LDA topic model variant.Evaluation indicates the algorithm improves on strong baselines.